Artificial neural network (ANN) based multi-parameter inversion method is proposed to characterize transversely isotropic composite lamina using Lamb wave group velocity measurements. The ANN is first trained using numerical simulations and known micromechanics based formulae before being deployed on experimental samples. The group velocities obtained from the experiments were fed to the trained network. The network so trained, predicted the elastic properties, fiber volume fraction, and density. © The Author(s) 2011.

Krishnan Balasubramaniam

Department of Mechanical Engineering

Artificial neural network

COMPOSITE LAMINA

Elastic properties

FIBER VOLUME FRACTIONS

Group velocities

Inversion methods

Lamb wave

LAMINA PROPERTIES

Multiparameters

Transversely isotropic

Light velocity

Neural Networks

Ultrasonic waves

Velocity measurement

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Artificial neural network based multi-parameter inversion for the characterization of transversely isotropic composite lamina using velocity measurements of Lamb waves

Journal of Composite Materials

The reconstruction of all nine unknown elastic moduli of orthotropic plate structures has been achieved using a single-transmitter-multiple-receiver (STMR) compact structural health monitoring (SHM) array. This method uses the velocity measurement of the fundamental guided Lamb wave modes (S0 and A 0), generated from a central transmitter, and received by a sparse array of receivers that encircle the transmitter. The measured velocities are then used in an inversion algorithm based on genetic algorithms. A prototype compact STMR array was developed and used in the measurement. Simulated data were used to demonstrate the feasibility of the technique. Experiments were conducted on 3.15 mm graphite-epoxy composite plate using a PZT based STMR array as well as laser vibrometer based displacement measurement. Experimental Lamb wave velocity data were used to validate the present technique. This technique finds application in the areas of material characterization and SHM of anisotropic plate-like structures used in aerospace and automobile components made using fiber reinforced composites. © IOP Publishing Ltd.

Smart Materials and Structures

Genetic algorithm based reconstruction of the elastic moduli of orthotropic plates using an ultrasonic guided wave single-transmitter-multiple-receiver SHM array

Platelike structures, made of composites, are being increasingly used for fabricating aircraft wings and other aircraft substructures. Continuous monitoring of the health of these structures would aid the reliable operation of aircrafts. This paper considers the use of a Lamb wave based structural health monitoring (SHM) system to identify and locate defects in large multilayered composite plates. The SHM system comprises of a single transmitter and multiple receivers, coupled to one side of the plate that send and receive Lamb waves. The proposed algorithm processes the data collected from the receivers and generates a reconstructed image of the material state of the composite plate. The algorithm is based on phased addition in the frequency domain to compensate for the dispersion of Lamb waves. In addition, small deviations from circularity of the slowness curves of Lamb wave modes, due to anisotropy, are corrected for by assuming that the phase and group velocity directions coincide locally. Experiments were performed on an anisotropic multilayered composite plate containing a single defect. Reconstruction of the defect is carried out using data for a weakly anisotropic Lamb wave mode as a proof of concept of the proposed algorithm. © 2006 Acoustical Society of America.

Journal of the Acoustical Society of America

A phase reconstruction algorithm for Lamb wave based structural health monitoring of anisotropic multilayered composite plates

Lamb wave tomography offers a new dimension to the challenging field of in situ health monitoring of structures. The possibility of constructing tomograms from a network of sensors generating and sensing Lamb waves in thin, multi-layered, anisotropic composite plates is explored in the present study. It is shown that improved tomograms result when the anisotropic and attenuative characteristics of composite plates are accounted for by (a) using the newly identified energy of the earliest Lamb wave signals as the reconstruction parameter, (b) modifying the sensor configuration from conventional geometries (and thereby also optimizing the number of sensors), and (c) normalizing the Lamb wave energy data of the defective sample with respect to that of the defect-free sample.

Structural health monitoring of composite structures using Lamb wave tomography

An Introduction to Reservoir Simulation Using MATLAB/GNU Octave

The MATLAB Reservoir Simulation Toolbox

Principles of Composite Material Mechanics

Journal	Journal of Composite Materials
ISSN	00219983
Open Access	No